{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e9f9fe6-d2e8-4a74-87e1-39c12d5079f3",
   "metadata": {},
   "source": [
    "# TSMixer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f52dab90-22b0-4901-a027-29ce9d79d104",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "574bc0fc-cca5-4f00-b2c7-a8c77aba6ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "class DataLoader:\n",
    "\n",
    "  def __init__(self, batch_size, seq_len, pred_len):\n",
    "    self.batch_size = batch_size\n",
    "    self.seq_len = seq_len\n",
    "    self.pred_len = pred_len\n",
    "    self.target_slice = slice(0, None)\n",
    "\n",
    "    self._read_data()\n",
    "\n",
    "  def _read_data(self):\n",
    "\n",
    "    filepath = ('data/ETTh1_original.csv')\n",
    "\n",
    "    df_raw = pd.read_csv(filepath)\n",
    "    df = df_raw.set_index('date')\n",
    "\n",
    "    # split train/valid/test\n",
    "    n = len(df)\n",
    "    train_end = int(n * 0.7)\n",
    "    val_end = n - int(n * 0.2)\n",
    "    test_end = n\n",
    "\n",
    "    train_df = df[:train_end]\n",
    "    val_df = df[train_end - self.seq_len : val_end]\n",
    "    test_df = df[val_end - self.seq_len : test_end]\n",
    "\n",
    "    # standardize by training set\n",
    "    self.scaler = StandardScaler()\n",
    "    self.scaler.fit(train_df.values)\n",
    "\n",
    "    def scale_df(df, scaler):\n",
    "      data = scaler.transform(df.values)\n",
    "      return pd.DataFrame(data, index=df.index, columns=df.columns)\n",
    "\n",
    "    self.train_df = scale_df(train_df, self.scaler)\n",
    "    self.val_df = scale_df(val_df, self.scaler)\n",
    "    self.test_df = scale_df(test_df, self.scaler)\n",
    "    self.n_feature = self.train_df.shape[-1]\n",
    "\n",
    "  def _split_window(self, data):\n",
    "    inputs = data[:, : self.seq_len, :]\n",
    "    labels = data[:, self.seq_len :, self.target_slice]\n",
    "\n",
    "    inputs.set_shape([None, self.seq_len, None])\n",
    "    labels.set_shape([None, self.pred_len, None])\n",
    "    return inputs, labels\n",
    "\n",
    "  def _make_dataset(self, data, shuffle=True):\n",
    "    data = np.array(data, dtype=np.float32)\n",
    "    ds = tf.keras.utils.timeseries_dataset_from_array(\n",
    "        data=data,\n",
    "        targets=None,\n",
    "        sequence_length=(self.seq_len + self.pred_len),\n",
    "        sequence_stride=1,\n",
    "        shuffle=shuffle,\n",
    "        batch_size=self.batch_size,\n",
    "    )\n",
    "    ds = ds.map(self._split_window)\n",
    "    return ds\n",
    "\n",
    "  def inverse_transform(self, data):\n",
    "    return self.scaler.inverse_transform(data)\n",
    "\n",
    "  def get_train(self, shuffle=True):\n",
    "    return self._make_dataset(self.train_df, shuffle=shuffle)\n",
    "\n",
    "  def get_val(self):\n",
    "    return self._make_dataset(self.val_df, shuffle=False)\n",
    "\n",
    "  def get_test(self):\n",
    "    return self._make_dataset(self.test_df, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e7c8aa7-21f8-4040-852a-264856c50b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = DataLoader(batch_size=32, seq_len=512, pred_len=96)\n",
    "\n",
    "train_data = data_loader.get_train()\n",
    "val_data = data_loader.get_val()\n",
    "test_data = data_loader.get_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18b0f743-045a-48f7-a058-22bbf6b0e690",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers\n",
    "\n",
    "def res_block(inputs, ff_dim):\n",
    "\n",
    "  norm = layers.BatchNormalization\n",
    "\n",
    "  # Time mixing\n",
    "  x = norm(axis=[-2, -1])(inputs)\n",
    "  x = tf.transpose(x, perm=[0, 2, 1])  # [Batch, Channel, Input Length]\n",
    "  x = layers.Dense(x.shape[-1], activation='relu')(x)\n",
    "  x = tf.transpose(x, perm=[0, 2, 1])  # [Batch, Input Length, Channel]\n",
    "  x = layers.Dropout(0.7)(x)\n",
    "  res = x + inputs\n",
    "\n",
    "  # Feature mixing\n",
    "  x = norm(axis=[-2, -1])(res)\n",
    "  x = layers.Dense(ff_dim, activation='relu')(x)  # [Batch, Input Length, FF_Dim]\n",
    "  x = layers.Dropout(0.7)(x)\n",
    "  x = layers.Dense(inputs.shape[-1])(x)  # [Batch, Input Length, Channel]\n",
    "  x = layers.Dropout(0.7)(x)\n",
    "  return x + res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63471461-7719-44fa-ba92-a2121034557b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(\n",
    "    input_shape,\n",
    "    pred_len,\n",
    "    n_block,\n",
    "    ff_dim,\n",
    "    target_slice,\n",
    "):\n",
    "\n",
    "  inputs = tf.keras.Input(shape=input_shape)\n",
    "  x = inputs  # [Batch, Input Length, Channel]\n",
    "  for _ in range(n_block):\n",
    "    x = res_block(x, ff_dim)\n",
    "\n",
    "  if target_slice:\n",
    "    x = x[:, :, target_slice]\n",
    "\n",
    "  # Temporal projection\n",
    "  x = tf.transpose(x, perm=[0, 2, 1])  # [Batch, Channel, Input Length]\n",
    "  x = layers.Dense(pred_len)(x)  # [Batch, Channel, Output Length]\n",
    "  outputs = tf.transpose(x, perm=[0, 2, 1])  # [Batch, Output Length, Channel])\n",
    "\n",
    "  return tf.keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ec105e2-0f41-4790-ad81-5494fa78c55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model(\n",
    "    input_shape=(512, data_loader.n_feature),\n",
    "    pred_len=96,\n",
    "    n_block=8,\n",
    "    ff_dim=64,\n",
    "    target_slice=data_loader.target_slice\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a82626a-da6d-4f9e-bc17-6791bb3ba567",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "363/363 [==============================] - 54s 126ms/step - loss: 27.9493 - mae: 4.0994 - val_loss: 0.9159 - val_mae: 0.7489\n",
      "Epoch 2/30\n",
      "363/363 [==============================] - 46s 126ms/step - loss: 11.3880 - mae: 2.6455 - val_loss: 0.7342 - val_mae: 0.6692\n",
      "Epoch 3/30\n",
      "363/363 [==============================] - 45s 124ms/step - loss: 6.5457 - mae: 2.0049 - val_loss: 0.6726 - val_mae: 0.6389\n",
      "Epoch 4/30\n",
      "363/363 [==============================] - 44s 122ms/step - loss: 4.3104 - mae: 1.6230 - val_loss: 0.6248 - val_mae: 0.6115\n",
      "Epoch 5/30\n",
      "363/363 [==============================] - 44s 122ms/step - loss: 3.0450 - mae: 1.3605 - val_loss: 0.5863 - val_mae: 0.5883\n",
      "Epoch 6/30\n",
      "363/363 [==============================] - 44s 121ms/step - loss: 2.2560 - mae: 1.1672 - val_loss: 0.5508 - val_mae: 0.5640\n",
      "Epoch 7/30\n",
      "363/363 [==============================] - 46s 128ms/step - loss: 1.7369 - mae: 1.0203 - val_loss: 0.5205 - val_mae: 0.5423\n",
      "Epoch 8/30\n",
      "363/363 [==============================] - 45s 123ms/step - loss: 1.3764 - mae: 0.9046 - val_loss: 0.5094 - val_mae: 0.5338\n",
      "Epoch 9/30\n",
      "363/363 [==============================] - 44s 122ms/step - loss: 1.1268 - mae: 0.8150 - val_loss: 0.4762 - val_mae: 0.5089\n",
      "Epoch 10/30\n",
      "363/363 [==============================] - 44s 122ms/step - loss: 0.9382 - mae: 0.7396 - val_loss: 0.4622 - val_mae: 0.4973\n",
      "Epoch 11/30\n",
      "363/363 [==============================] - 45s 123ms/step - loss: 0.8044 - mae: 0.6815 - val_loss: 0.4616 - val_mae: 0.4961\n",
      "Epoch 12/30\n",
      "363/363 [==============================] - 45s 123ms/step - loss: 0.7034 - mae: 0.6338 - val_loss: 0.4385 - val_mae: 0.4782\n",
      "Epoch 13/30\n",
      "363/363 [==============================] - 44s 120ms/step - loss: 0.6346 - mae: 0.5993 - val_loss: 0.4406 - val_mae: 0.4793\n",
      "Epoch 14/30\n",
      "363/363 [==============================] - 44s 121ms/step - loss: 0.5737 - mae: 0.5668 - val_loss: 0.4339 - val_mae: 0.4734\n",
      "Epoch 15/30\n",
      "363/363 [==============================] - 45s 123ms/step - loss: 0.5294 - mae: 0.5418 - val_loss: 0.4215 - val_mae: 0.4633\n",
      "Epoch 16/30\n",
      "363/363 [==============================] - 43s 119ms/step - loss: 0.4986 - mae: 0.5237 - val_loss: 0.4247 - val_mae: 0.4653\n",
      "Epoch 17/30\n",
      "363/363 [==============================] - 44s 121ms/step - loss: 0.4761 - mae: 0.5100 - val_loss: 0.3954 - val_mae: 0.4436\n",
      "Epoch 18/30\n",
      "363/363 [==============================] - 44s 120ms/step - loss: 0.4586 - mae: 0.4986 - val_loss: 0.4413 - val_mae: 0.4772\n",
      "Epoch 19/30\n",
      "363/363 [==============================] - 44s 120ms/step - loss: 0.4426 - mae: 0.4885 - val_loss: 0.4042 - val_mae: 0.4486\n",
      "Epoch 20/30\n",
      "363/363 [==============================] - 44s 120ms/step - loss: 0.4306 - mae: 0.4803 - val_loss: 0.4248 - val_mae: 0.4637\n",
      "Training finished in 899.1872563362122 secconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "tf.keras.utils.set_random_seed(42)\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam(1e-4)\n",
    "\n",
    "model.compile(optimizer, loss='mse', metrics=['mae'])\n",
    "\n",
    "checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath='tsmixer_checkpoints/',\n",
    "    vebose=1,\n",
    "    save_best_only=True,\n",
    "    save_weights_only=True\n",
    ")\n",
    "\n",
    "early_stop_callback = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    patience=3\n",
    ")\n",
    "\n",
    "start_training_time = time.time()\n",
    "\n",
    "history = model.fit(\n",
    "    train_data,\n",
    "    epochs= 30,\n",
    "    validation_data=val_data,\n",
    "    callbacks=[checkpoint_callback, early_stop_callback]\n",
    ")\n",
    "\n",
    "end_training_time = time.time()\n",
    "elasped_training_time = end_training_time - start_training_time\n",
    "print(f'Training finished in {elasped_training_time} secconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d4cf391-0321-4bf3-b2cf-4b6900085d31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x24585a47af0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_epoch = np.argmin(history.history['val_loss'])\n",
    "\n",
    "model.load_weights(\"tsmixer_checkpoints/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1611f062-51f1-4950-8e1e-2540f2221f9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "106/106 [==============================] - 3s 25ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(96, 7)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test_data)\n",
    "\n",
    "scaled_preds = predictions[-1,:,:]\n",
    "\n",
    "scaled_preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9eb235af-b9f3-452f-81d7-7011ae5de70c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['HUFL', 'HULL', 'MUFL', 'MULL', 'LUFL', 'LULL', 'OT']\n",
    "\n",
    "scaled_preds_df = pd.DataFrame(scaled_preds)\n",
    "scaled_preds_df.columns = cols\n",
    "\n",
    "preds = data_loader.inverse_transform(scaled_preds)\n",
    "\n",
    "preds_df = pd.DataFrame(preds)\n",
    "preds_df.columns = cols\n",
    "\n",
    "preds_df.to_csv('data/tsmixer_preds_etth1_h96.csv', index=False, header=True)\n",
    "scaled_preds_df.to_csv('data/tsmixer_scaled_preds_etth1_h96.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c66b6f6f-69b7-4506-b698-71f6287a78e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.783643</td>\n",
       "      <td>1.828940</td>\n",
       "      <td>6.611066</td>\n",
       "      <td>0.410760</td>\n",
       "      <td>2.775144</td>\n",
       "      <td>0.976482</td>\n",
       "      <td>6.176808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.346516</td>\n",
       "      <td>1.945308</td>\n",
       "      <td>7.340568</td>\n",
       "      <td>0.546736</td>\n",
       "      <td>2.691021</td>\n",
       "      <td>0.944694</td>\n",
       "      <td>5.647042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.133471</td>\n",
       "      <td>2.252884</td>\n",
       "      <td>7.656474</td>\n",
       "      <td>0.728890</td>\n",
       "      <td>2.738519</td>\n",
       "      <td>0.992854</td>\n",
       "      <td>5.419943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.133713</td>\n",
       "      <td>2.657871</td>\n",
       "      <td>7.635908</td>\n",
       "      <td>0.993395</td>\n",
       "      <td>2.916682</td>\n",
       "      <td>1.051881</td>\n",
       "      <td>5.555630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.121804</td>\n",
       "      <td>3.026586</td>\n",
       "      <td>8.378697</td>\n",
       "      <td>1.277050</td>\n",
       "      <td>3.137067</td>\n",
       "      <td>1.118916</td>\n",
       "      <td>5.940153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        HUFL      HULL      MUFL      MULL      LUFL      LULL        OT\n",
       "0   9.783643  1.828940  6.611066  0.410760  2.775144  0.976482  6.176808\n",
       "1  10.346516  1.945308  7.340568  0.546736  2.691021  0.944694  5.647042\n",
       "2  11.133471  2.252884  7.656474  0.728890  2.738519  0.992854  5.419943\n",
       "3  11.133713  2.657871  7.635908  0.993395  2.916682  1.051881  5.555630\n",
       "4  12.121804  3.026586  8.378697  1.277050  3.137067  1.118916  5.940153"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4be07ee4-fbe7-4da1-b6c6-70cd668c0d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.368250</td>\n",
       "      <td>-0.060601</td>\n",
       "      <td>0.334844</td>\n",
       "      <td>-0.146730</td>\n",
       "      <td>-0.118572</td>\n",
       "      <td>0.295890</td>\n",
       "      <td>-1.211947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.456878</td>\n",
       "      <td>-0.005528</td>\n",
       "      <td>0.453329</td>\n",
       "      <td>-0.076186</td>\n",
       "      <td>-0.189350</td>\n",
       "      <td>0.247901</td>\n",
       "      <td>-1.275404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.580789</td>\n",
       "      <td>0.140036</td>\n",
       "      <td>0.504638</td>\n",
       "      <td>0.018313</td>\n",
       "      <td>-0.149387</td>\n",
       "      <td>0.320605</td>\n",
       "      <td>-1.302606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.580827</td>\n",
       "      <td>0.331701</td>\n",
       "      <td>0.501298</td>\n",
       "      <td>0.155535</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.409713</td>\n",
       "      <td>-1.286353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.736408</td>\n",
       "      <td>0.506200</td>\n",
       "      <td>0.621941</td>\n",
       "      <td>0.302692</td>\n",
       "      <td>0.185933</td>\n",
       "      <td>0.510911</td>\n",
       "      <td>-1.240294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HUFL      HULL      MUFL      MULL      LUFL      LULL        OT\n",
       "0  0.368250 -0.060601  0.334844 -0.146730 -0.118572  0.295890 -1.211947\n",
       "1  0.456878 -0.005528  0.453329 -0.076186 -0.189350  0.247901 -1.275404\n",
       "2  0.580789  0.140036  0.504638  0.018313 -0.149387  0.320605 -1.302606\n",
       "3  0.580827  0.331701  0.501298  0.155535  0.000512  0.409713 -1.286353\n",
       "4  0.736408  0.506200  0.621941  0.302692  0.185933  0.510911 -1.240294"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a84d30c-6f2b-4a41-86a4-ebe86af04474",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a088b27-603e-40a3-a651-64d1a13f4165",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d095d891-2cca-419a-82f0-077dcf8e7c4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6aee197f-7419-4187-83e0-8b382fcce171",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bb685b16",
   "metadata": {},
   "source": [
    "## N-HiTS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ec4122aa-36e1-4774-856e-c693bd35ac74",
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.models import NHITS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c812351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>5.827</td>\n",
       "      <td>2.009</td>\n",
       "      <td>1.599</td>\n",
       "      <td>0.462</td>\n",
       "      <td>4.203</td>\n",
       "      <td>1.340</td>\n",
       "      <td>30.531000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-01 01:00:00</td>\n",
       "      <td>5.693</td>\n",
       "      <td>2.076</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.426</td>\n",
       "      <td>4.142</td>\n",
       "      <td>1.371</td>\n",
       "      <td>27.787001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-01 02:00:00</td>\n",
       "      <td>5.157</td>\n",
       "      <td>1.741</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.355</td>\n",
       "      <td>3.777</td>\n",
       "      <td>1.218</td>\n",
       "      <td>27.787001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-07-01 03:00:00</td>\n",
       "      <td>5.090</td>\n",
       "      <td>1.942</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.391</td>\n",
       "      <td>3.807</td>\n",
       "      <td>1.279</td>\n",
       "      <td>25.044001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-07-01 04:00:00</td>\n",
       "      <td>5.358</td>\n",
       "      <td>1.942</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.462</td>\n",
       "      <td>3.868</td>\n",
       "      <td>1.279</td>\n",
       "      <td>21.948000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date   HUFL   HULL   MUFL   MULL   LUFL   LULL         OT\n",
       "0  2016-07-01 00:00:00  5.827  2.009  1.599  0.462  4.203  1.340  30.531000\n",
       "1  2016-07-01 01:00:00  5.693  2.076  1.492  0.426  4.142  1.371  27.787001\n",
       "2  2016-07-01 02:00:00  5.157  1.741  1.279  0.355  3.777  1.218  27.787001\n",
       "3  2016-07-01 03:00:00  5.090  1.942  1.279  0.391  3.807  1.279  25.044001\n",
       "4  2016-07-01 04:00:00  5.358  1.942  1.492  0.462  3.868  1.279  21.948000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/ETTh1_original.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "613f67b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>unique_id</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>HUFL</td>\n",
       "      <td>5.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-01 01:00:00</td>\n",
       "      <td>HUFL</td>\n",
       "      <td>5.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-01 02:00:00</td>\n",
       "      <td>HUFL</td>\n",
       "      <td>5.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-07-01 03:00:00</td>\n",
       "      <td>HUFL</td>\n",
       "      <td>5.090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-07-01 04:00:00</td>\n",
       "      <td>HUFL</td>\n",
       "      <td>5.358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds unique_id      y\n",
       "0 2016-07-01 00:00:00      HUFL  5.827\n",
       "1 2016-07-01 01:00:00      HUFL  5.693\n",
       "2 2016-07-01 02:00:00      HUFL  5.157\n",
       "3 2016-07-01 03:00:00      HUFL  5.090\n",
       "4 2016-07-01 04:00:00      HUFL  5.358"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_melt = ['date', 'HUFL', 'HULL', 'MUFL', 'MULL', 'LUFL', 'LULL', 'OT']\n",
    "\n",
    "melted_df = df.melt(id_vars=['date'], value_vars=columns_to_melt, var_name='unique_id', value_name='y')\n",
    "\n",
    "melted_df.rename(columns={'date': 'ds'}, inplace=True)\n",
    "\n",
    "melted_df['ds'] = pd.to_datetime(melted_df['ds'])\n",
    "\n",
    "melted_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30d0ea2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 96\n",
    "\n",
    "models = [\n",
    "    NHITS(h=horizon, input_size=512, max_steps=30),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "51622bdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "nf = NeuralForecast(models=models, freq='H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f6bff883",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29: 100%|█████████████████| 1/1 [00:00<00:00,  2.92it/s, v_num=98, train_loss_step=1.910, train_loss_epoch=1.930]\n",
      "Validation: 0it [00:00, ?it/s]\u001b[A\n",
      "Validation:   0%|                                                                                | 0/1 [00:00<?, ?it/s]\u001b[A\n",
      "Validation DataLoader 0:   0%|                                                                   | 0/1 [00:00<?, ?it/s]\u001b[A\n",
      "Epoch 29: 100%|█| 1/1 [00:01<00:00,  1.28s/it, v_num=98, train_loss_step=1.910, train_loss_epoch=1.930, valid_loss=2.22\u001b[A\n",
      "Epoch 29: 100%|█| 1/1 [00:01<00:00,  1.29s/it, v_num=98, train_loss_step=1.910, train_loss_epoch=1.910, valid_loss=2.22\u001b[A\n",
      "Predicting DataLoader 0: 100%|███████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  2.16it/s]\n"
     ]
    }
   ],
   "source": [
    "n_preds_df = nf.cross_validation(df=melted_df, val_size=int(0.2*len(df)), test_size=int(0.1*len(df)), n_windows=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e972a341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-04-15 06:00:00</td>\n",
       "      <td>2018-04-15 05:00:00</td>\n",
       "      <td>13.136290</td>\n",
       "      <td>15.874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-04-15 07:00:00</td>\n",
       "      <td>2018-04-15 05:00:00</td>\n",
       "      <td>11.774220</td>\n",
       "      <td>13.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-04-15 08:00:00</td>\n",
       "      <td>2018-04-15 05:00:00</td>\n",
       "      <td>8.583214</td>\n",
       "      <td>3.148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-04-15 09:00:00</td>\n",
       "      <td>2018-04-15 05:00:00</td>\n",
       "      <td>5.306442</td>\n",
       "      <td>-5.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-04-15 10:00:00</td>\n",
       "      <td>2018-04-15 05:00:00</td>\n",
       "      <td>1.011492</td>\n",
       "      <td>-10.784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds              cutoff      NHITS       y\n",
       "0      HUFL 2018-04-15 06:00:00 2018-04-15 05:00:00  13.136290  15.874\n",
       "1      HUFL 2018-04-15 07:00:00 2018-04-15 05:00:00  11.774220  13.128\n",
       "2      HUFL 2018-04-15 08:00:00 2018-04-15 05:00:00   8.583214   3.148\n",
       "3      HUFL 2018-04-15 09:00:00 2018-04-15 05:00:00   5.306442  -5.693\n",
       "4      HUFL 2018-04-15 10:00:00 2018-04-15 05:00:00   1.011492 -10.784"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_preds_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a2138ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-06-22 20:00:00 2018-06-26 19:00:00\n"
     ]
    }
   ],
   "source": [
    "df['date'][-96:] = pd.to_datetime(df['date'][-96:])\n",
    "\n",
    "max_date = df['date'][-96:].max()\n",
    "min_date = df['date'][-96:].min()\n",
    "\n",
    "print(min_date, max_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f32c7d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>148991</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 20:00:00</td>\n",
       "      <td>2018-06-18 20:00:00</td>\n",
       "      <td>10.835604</td>\n",
       "      <td>6.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149086</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 20:00:00</td>\n",
       "      <td>2018-06-18 21:00:00</td>\n",
       "      <td>12.152081</td>\n",
       "      <td>6.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149087</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 21:00:00</td>\n",
       "      <td>2018-06-18 21:00:00</td>\n",
       "      <td>12.602386</td>\n",
       "      <td>6.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149181</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 20:00:00</td>\n",
       "      <td>2018-06-18 22:00:00</td>\n",
       "      <td>10.489227</td>\n",
       "      <td>6.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149182</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 21:00:00</td>\n",
       "      <td>2018-06-18 22:00:00</td>\n",
       "      <td>11.020892</td>\n",
       "      <td>6.564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106779</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 15:00:00</td>\n",
       "      <td>2018-06-22 19:00:00</td>\n",
       "      <td>6.169090</td>\n",
       "      <td>10.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106780</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 16:00:00</td>\n",
       "      <td>2018-06-22 19:00:00</td>\n",
       "      <td>5.907017</td>\n",
       "      <td>11.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106781</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 17:00:00</td>\n",
       "      <td>2018-06-22 19:00:00</td>\n",
       "      <td>5.559777</td>\n",
       "      <td>10.271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106782</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 18:00:00</td>\n",
       "      <td>2018-06-22 19:00:00</td>\n",
       "      <td>5.709448</td>\n",
       "      <td>9.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106783</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 19:00:00</td>\n",
       "      <td>2018-06-22 19:00:00</td>\n",
       "      <td>5.804244</td>\n",
       "      <td>9.567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32592 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        unique_id                  ds              cutoff      NHITS       y\n",
       "148991       HUFL 2018-06-22 20:00:00 2018-06-18 20:00:00  10.835604   6.564\n",
       "149086       HUFL 2018-06-22 20:00:00 2018-06-18 21:00:00  12.152081   6.564\n",
       "149087       HUFL 2018-06-22 21:00:00 2018-06-18 21:00:00  12.602386   6.564\n",
       "149181       HUFL 2018-06-22 20:00:00 2018-06-18 22:00:00  10.489227   6.564\n",
       "149182       HUFL 2018-06-22 21:00:00 2018-06-18 22:00:00  11.020892   6.564\n",
       "...           ...                 ...                 ...        ...     ...\n",
       "1106779        OT 2018-06-26 15:00:00 2018-06-22 19:00:00   6.169090  10.904\n",
       "1106780        OT 2018-06-26 16:00:00 2018-06-22 19:00:00   5.907017  11.044\n",
       "1106781        OT 2018-06-26 17:00:00 2018-06-22 19:00:00   5.559777  10.271\n",
       "1106782        OT 2018-06-26 18:00:00 2018-06-22 19:00:00   5.709448   9.778\n",
       "1106783        OT 2018-06-26 19:00:00 2018-06-22 19:00:00   5.804244   9.567\n",
       "\n",
       "[32592 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_n_preds_df = n_preds_df[(n_preds_df['ds'] >= min_date) & (n_preds_df['ds'] <= max_date)]\n",
    "\n",
    "last_n_preds_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a05b61f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['HUFL', 'HULL', 'MUFL', 'MULL', 'LUFL', 'LULL', 'OT']\n",
    "\n",
    "clean_last_n_preds_df = pd.DataFrame()\n",
    "\n",
    "for col in cols:\n",
    "    temp_df = last_n_preds_df[last_n_preds_df['unique_id'] == col].drop_duplicates(subset='ds', keep='first')\n",
    "    clean_last_n_preds_df = pd.concat([clean_last_n_preds_df, temp_df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9f811633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 20:00:00</td>\n",
       "      <td>2018-06-18 20:00:00</td>\n",
       "      <td>10.835604</td>\n",
       "      <td>6.564000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 21:00:00</td>\n",
       "      <td>2018-06-18 21:00:00</td>\n",
       "      <td>12.602386</td>\n",
       "      <td>6.564000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 22:00:00</td>\n",
       "      <td>2018-06-18 22:00:00</td>\n",
       "      <td>11.378361</td>\n",
       "      <td>9.042000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-22 23:00:00</td>\n",
       "      <td>2018-06-18 23:00:00</td>\n",
       "      <td>12.418781</td>\n",
       "      <td>11.119000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2018-06-23 00:00:00</td>\n",
       "      <td>2018-06-19 00:00:00</td>\n",
       "      <td>11.536001</td>\n",
       "      <td>16.476999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 15:00:00</td>\n",
       "      <td>2018-06-22 15:00:00</td>\n",
       "      <td>4.331094</td>\n",
       "      <td>10.904000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 16:00:00</td>\n",
       "      <td>2018-06-22 16:00:00</td>\n",
       "      <td>5.626397</td>\n",
       "      <td>11.044000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 17:00:00</td>\n",
       "      <td>2018-06-22 17:00:00</td>\n",
       "      <td>4.128483</td>\n",
       "      <td>10.271000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 18:00:00</td>\n",
       "      <td>2018-06-22 18:00:00</td>\n",
       "      <td>4.590957</td>\n",
       "      <td>9.778000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-06-26 19:00:00</td>\n",
       "      <td>2018-06-22 19:00:00</td>\n",
       "      <td>5.804244</td>\n",
       "      <td>9.567000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>672 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    unique_id                  ds              cutoff      NHITS          y\n",
       "0        HUFL 2018-06-22 20:00:00 2018-06-18 20:00:00  10.835604   6.564000\n",
       "1        HUFL 2018-06-22 21:00:00 2018-06-18 21:00:00  12.602386   6.564000\n",
       "2        HUFL 2018-06-22 22:00:00 2018-06-18 22:00:00  11.378361   9.042000\n",
       "3        HUFL 2018-06-22 23:00:00 2018-06-18 23:00:00  12.418781  11.119000\n",
       "4        HUFL 2018-06-23 00:00:00 2018-06-19 00:00:00  11.536001  16.476999\n",
       "..        ...                 ...                 ...        ...        ...\n",
       "667        OT 2018-06-26 15:00:00 2018-06-22 15:00:00   4.331094  10.904000\n",
       "668        OT 2018-06-26 16:00:00 2018-06-22 16:00:00   5.626397  11.044000\n",
       "669        OT 2018-06-26 17:00:00 2018-06-22 17:00:00   4.128483  10.271000\n",
       "670        OT 2018-06-26 18:00:00 2018-06-22 18:00:00   4.590957   9.778000\n",
       "671        OT 2018-06-26 19:00:00 2018-06-22 19:00:00   5.804244   9.567000\n",
       "\n",
       "[672 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_last_n_preds_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2dcadb95",
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_last_n_preds_df.to_csv('data/nhits_preds_etth1_h96.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2651674",
   "metadata": {},
   "source": [
    "## Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ef0b5b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "nhits_preds = pd.read_csv('data/nhits_preds_etth1_h96.csv')\n",
    "tsmixer_preds = pd.read_csv('data/tsmixer_preds_etth1_h96.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "55352723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.783643</td>\n",
       "      <td>1.828940</td>\n",
       "      <td>6.611066</td>\n",
       "      <td>0.410760</td>\n",
       "      <td>2.775144</td>\n",
       "      <td>0.976482</td>\n",
       "      <td>6.176808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.346516</td>\n",
       "      <td>1.945308</td>\n",
       "      <td>7.340568</td>\n",
       "      <td>0.546736</td>\n",
       "      <td>2.691021</td>\n",
       "      <td>0.944694</td>\n",
       "      <td>5.647042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.133471</td>\n",
       "      <td>2.252884</td>\n",
       "      <td>7.656474</td>\n",
       "      <td>0.728890</td>\n",
       "      <td>2.738519</td>\n",
       "      <td>0.992854</td>\n",
       "      <td>5.419943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.133713</td>\n",
       "      <td>2.657872</td>\n",
       "      <td>7.635908</td>\n",
       "      <td>0.993395</td>\n",
       "      <td>2.916682</td>\n",
       "      <td>1.051881</td>\n",
       "      <td>5.555630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.121804</td>\n",
       "      <td>3.026586</td>\n",
       "      <td>8.378697</td>\n",
       "      <td>1.277050</td>\n",
       "      <td>3.137067</td>\n",
       "      <td>1.118916</td>\n",
       "      <td>5.940153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>-3.986025</td>\n",
       "      <td>2.388264</td>\n",
       "      <td>-6.330092</td>\n",
       "      <td>0.850562</td>\n",
       "      <td>2.652524</td>\n",
       "      <td>1.100195</td>\n",
       "      <td>9.882478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>-0.081644</td>\n",
       "      <td>2.174035</td>\n",
       "      <td>-2.532439</td>\n",
       "      <td>0.653812</td>\n",
       "      <td>2.748767</td>\n",
       "      <td>1.120828</td>\n",
       "      <td>9.157974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>3.744680</td>\n",
       "      <td>1.827072</td>\n",
       "      <td>0.932927</td>\n",
       "      <td>0.433281</td>\n",
       "      <td>2.677535</td>\n",
       "      <td>1.053055</td>\n",
       "      <td>8.265033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>6.322721</td>\n",
       "      <td>1.712198</td>\n",
       "      <td>3.394067</td>\n",
       "      <td>0.318376</td>\n",
       "      <td>2.642339</td>\n",
       "      <td>1.019517</td>\n",
       "      <td>7.820487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>8.469596</td>\n",
       "      <td>1.577753</td>\n",
       "      <td>5.464374</td>\n",
       "      <td>0.221544</td>\n",
       "      <td>2.617654</td>\n",
       "      <td>0.951356</td>\n",
       "      <td>7.751969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         HUFL      HULL      MUFL      MULL      LUFL      LULL        OT\n",
       "0    9.783643  1.828940  6.611066  0.410760  2.775144  0.976482  6.176808\n",
       "1   10.346516  1.945308  7.340568  0.546736  2.691021  0.944694  5.647042\n",
       "2   11.133471  2.252884  7.656474  0.728890  2.738519  0.992854  5.419943\n",
       "3   11.133713  2.657872  7.635908  0.993395  2.916682  1.051881  5.555630\n",
       "4   12.121804  3.026586  8.378697  1.277050  3.137067  1.118916  5.940153\n",
       "..        ...       ...       ...       ...       ...       ...       ...\n",
       "91  -3.986025  2.388264 -6.330092  0.850562  2.652524  1.100195  9.882478\n",
       "92  -0.081644  2.174035 -2.532439  0.653812  2.748767  1.120828  9.157974\n",
       "93   3.744680  1.827072  0.932927  0.433281  2.677535  1.053055  8.265033\n",
       "94   6.322721  1.712198  3.394067  0.318376  2.642339  1.019517  7.820487\n",
       "95   8.469596  1.577753  5.464374  0.221544  2.617654  0.951356  7.751969\n",
       "\n",
       "[96 rows x 7 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsmixer_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "79c59bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>HUFL</th>\n",
       "      <th>HULL</th>\n",
       "      <th>MUFL</th>\n",
       "      <th>MULL</th>\n",
       "      <th>LUFL</th>\n",
       "      <th>LULL</th>\n",
       "      <th>OT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>5.827</td>\n",
       "      <td>2.009</td>\n",
       "      <td>1.599</td>\n",
       "      <td>0.462</td>\n",
       "      <td>4.203</td>\n",
       "      <td>1.340</td>\n",
       "      <td>30.531000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-01 01:00:00</td>\n",
       "      <td>5.693</td>\n",
       "      <td>2.076</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.426</td>\n",
       "      <td>4.142</td>\n",
       "      <td>1.371</td>\n",
       "      <td>27.787001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-07-01 02:00:00</td>\n",
       "      <td>5.157</td>\n",
       "      <td>1.741</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.355</td>\n",
       "      <td>3.777</td>\n",
       "      <td>1.218</td>\n",
       "      <td>27.787001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-07-01 03:00:00</td>\n",
       "      <td>5.090</td>\n",
       "      <td>1.942</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.391</td>\n",
       "      <td>3.807</td>\n",
       "      <td>1.279</td>\n",
       "      <td>25.044001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-07-01 04:00:00</td>\n",
       "      <td>5.358</td>\n",
       "      <td>1.942</td>\n",
       "      <td>1.492</td>\n",
       "      <td>0.462</td>\n",
       "      <td>3.868</td>\n",
       "      <td>1.279</td>\n",
       "      <td>21.948000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date   HUFL   HULL   MUFL   MULL   LUFL   LULL         OT\n",
       "0  2016-07-01 00:00:00  5.827  2.009  1.599  0.462  4.203  1.340  30.531000\n",
       "1  2016-07-01 01:00:00  5.693  2.076  1.492  0.426  4.142  1.371  27.787001\n",
       "2  2016-07-01 02:00:00  5.157  1.741  1.279  0.355  3.777  1.218  27.787001\n",
       "3  2016-07-01 03:00:00  5.090  1.942  1.279  0.391  3.807  1.279  25.044001\n",
       "4  2016-07-01 04:00:00  5.358  1.942  1.492  0.462  3.868  1.279  21.948000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dcd1a18e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols_to_plot = ['HUFL', 'HULL', 'MUFL', 'MULL']\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(12,8))\n",
    "\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    col = cols_to_plot[i]\n",
    "    \n",
    "    nhits_df = nhits_preds[nhits_preds['unique_id'] == col] \n",
    "    \n",
    "    ax.plot(df['date'][-96:], df[col][-96:])\n",
    "    ax.plot(df['date'][-96:], tsmixer_preds[col], label='TSMixer', ls='--', color='green')\n",
    "    ax.plot(df['date'][-96:], nhits_df['NHITS'], label='N-HiTS', ls=':', color='black')\n",
    "    \n",
    "    ax.legend(loc='best')\n",
    "    ax.set_xlabel('Time steps')\n",
    "    ax.set_ylabel('Value')\n",
    "    ax.set_title(col)\n",
    "    \n",
    "plt.tight_layout()\n",
    "fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1a2df29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7bad4_row0_col1, #T_7bad4_row1_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7bad4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7bad4_level0_col0\" class=\"col_heading level0 col0\" >N-HiTS</th>\n",
       "      <th id=\"T_7bad4_level0_col1\" class=\"col_heading level0 col1\" >TSMixer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7bad4_level0_row0\" class=\"row_heading level0 row0\" >mae</th>\n",
       "      <td id=\"T_7bad4_row0_col0\" class=\"data row0 col0\" >1.864861</td>\n",
       "      <td id=\"T_7bad4_row0_col1\" class=\"data row0 col1\" >1.611139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7bad4_level0_row1\" class=\"row_heading level0 row1\" >mse</th>\n",
       "      <td id=\"T_7bad4_row1_col0\" class=\"data row1 col0\" >10.093577</td>\n",
       "      <td id=\"T_7bad4_row1_col1\" class=\"data row1 col1\" >5.231349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x23587403490>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "y_actual = df.drop('date', axis=1)[-96:]\n",
    "\n",
    "data = {'N-HiTS': \n",
    "            [mean_absolute_error(nhits_preds['y'], nhits_preds['NHITS']), \n",
    "             mean_squared_error(nhits_preds['y'], nhits_preds['NHITS'])],\n",
    "       'TSMixer': \n",
    "            [mean_absolute_error(y_actual, tsmixer_preds), \n",
    "             mean_squared_error(y_actual, tsmixer_preds)]}\n",
    "\n",
    "metrics_df = pd.DataFrame(data=data)\n",
    "metrics_df.index = ['mae', 'mse']\n",
    "\n",
    "metrics_df.style.highlight_min(color='lightgreen', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fb4497c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
